Publication detail

Design of variable exponential forgetting for estimation of the statistics of the Normal distribution

DOKOUPIL, J. VÁCLAVEK, P.

Original Title

Design of variable exponential forgetting for estimation of the statistics of the Normal distribution

Type

conference paper

Language

English

Original Abstract

A recursive algorithm for estimating the statistics of the Normal distribution is designed, making it adaptive in the sense that the forgetting factor is driven by data. A mechanism to suppress obsolete information is proposed, following the principles of Bayesian decision-making. Specifically, the best combination of two time-evolution model hypotheses in terms of the geometric mean is performed. The first hypothesis assumes no change in the parameter evolution, while the second one assumes that all parameter changes are equally admitted. In order to provide data-driven forgetting, complementary probabilities assigned to each hypothesis are determined as the maximizers of the decision problem. Simulations, including a performance comparison with a recently proposed self-tuning estimator, are presented.

Keywords

estimation; forgetting factor; Kullback-Leibler divergence; Normal distribution

Authors

DOKOUPIL, J.; VÁCLAVEK, P.

Released

29. 12. 2016

Publisher

IEEE

ISBN

978-1-5090-1837-6

Book

55th Conference on Decision and Control

Pages from

1179

Pages to

1184

Pages count

6

URL

http://ieeexplore.ieee.org/document/7798426/

BibTex

@inproceedings{BUT130677,
  author="Jakub {Dokoupil} and Pavel {Václavek}",
  title="Design of variable exponential forgetting for estimation of the statistics of the Normal distribution",
  booktitle="55th Conference on Decision and Control",
  year="2016",
  pages="1179--1184",
  publisher="IEEE",
  doi="10.1109/CDC.2016.7798426",
  isbn="978-1-5090-1837-6",
  url="http://ieeexplore.ieee.org/document/7798426/"
}